Legionella pneumophila is a valuable tool in epidemiological studies and outbreak investigations of Legionnaires’ disease. In the L. pneumophila SBT scheme, mompS2 is one of seven genes that determine the sequence type (ST). The Legionella genome typically contains two copies of mompS (mompS1 and mompS2). When they are non-identical it can be challenging to determine the mompS2 allele, and subsequently the ST, from Illumina short-reads. In our collection of 233 L. pneumophila genomes, there were 62 STs, 18 of which carried non-identical mompS copies. Using short-reads, the mompS2 allele was misassembled or untypeable in several STs. Genomes belonging to ST154 and ST574, which carried mompS1 allele 7 and mompS2 allele 15, were assigned an incorrect mompS2 allele and/or mompS gene copy number when short-read assembled. For other isolates, mainly those carrying non-identical mompS copies, short-read assemblers occasionally failed to resolve the structure of the mompS-region, also resulting in untypeability from the short-read data. In this study, we wanted to understand the challenges we observed with calling the mompS2 allele from short-reads, assess if other short-read methods were able to resolve the mompS-region, and investigate the possibility of using long-reads to obtain the mompS alleles, and thereby perform L. pneumophila SBT from long-reads only. We found that the choice of short-read assembler had a major impact on resolving the mompS-region and thus SBT from short-reads, but no method consistently solved the mompS2 allele. By using Oxford Nanopore Technology (ONT) sequencing together with Trycycler and Medaka for long-read assembly and polishing we were able to resolve the mompS copies and correctly identify the mompS2 allele, in accordance with Sanger sequencing/EQA results for all tested isolates (n=35). The remaining six genes of the SBT profile could also be determined from the ONT-only reads. The STs called from ONT-only assemblies were also consistent with hybrid-assemblies of Illumina and ONT reads. We therefore propose ONT sequencing as an alternative method to perform L. pneumophila SBT to overcome the mompS challenge observed with short-reads. To facilitate this, we have developed ONTmompS (https://github.com/marithetland/ONTmompS), an in silico approach to determine L. pneumophila ST from long-read or hybrid assemblies.Sequence-based typing (SBT) of  When the bacteria colonize and multiply in man-made systems with favorable conditions for growth, it may pose a threat to human health, through inhalation of bacteria-contaminated aerosols  determined by the seven genes flaA, pilE, asd, mip, mompS, proA and neuA  has become the method of choice, providing both ST and superior information about genetic relatedness, and is today indispensable in surveillance and outbreak investigations (Sequence-based typing (SBT) is a valuable tool for source investigations and epidemiological studies of mparison . Althougigations . AnalyseL. pneumophila SBT scheme is that the mompS gene, which is used to determine the ST, is usually present with two copies in the genome, mompS1 and mompS2. Only mompS2 contributes to the ST . However, challenges with untypeability and resolution of repetitive regions due to the short read length has led to exploration of long-read sequencing as an alternative . At the mompS from short-reads. In diagnostics and surveillance of Legionella, time and cost is of the essence and generation of hybrid assemblies for all samples is not likely to be standard procedure. Therefore, the possibility of using ONT-only data to overcome the shortcomings observed in L. pneumophila SBT from short-reads is highly relevant and has to our knowledge not previously been investigated. In this study, we wanted to understand the challenges we observed using short-reads, assess if other short-read methods were able to resolve the mompS-region and investigate the possibility of using long-reads to obtain the mompS allele, and thereby perform L. pneumophila SBT from long-reads only.In our laboratory, we have observed several challenges related to calling 22.1Legionella, Stavanger University Hospital, Norway, we have a collection of Illumina sequenced clinical and environmental isolates, collected between 2001 and 2022 (n=233). Of these, 35 were selected for further analysis in this study, based on discrepancies between the mompS result from Illumina short-read and Sanger sequencing, presence of non-identical mompS copies or untypeability of mompS (n=27). Isolates with identical copies of mompS were included as controls (n=8).At the National Reference Laboratory for in silico SBT approach that we developed for assigning L. pneumophila ST (see section 3.3).A further 46 isolates were included later in the study to validate the ONTmompS 2.2L. pneumophila isolates had previously been Illumina short-read sequenced and the fastq-files deposited in our in-house sequence database. In short, DNA had been extracted using MagNA Pure 96 with the Pathogen Universal 200 4.0 purification protocol . Genomic libraries were prepared using Illumina DNA library prep and sequenced to ≥30X read depth using the Illumina MiSeq platform . Genomic libraries for long-read sequencing were prepared using the Kit12 chemistry and the ligation sequencing kit (SQK-LSK112) in combination with the R10.4 MinION flow cells (n=34), or the Kit9 ligation sequencing kit (SQK-LSK-109) in combination with the R9.4.1 flow cell (n=1) from ONT . All libraries were sequenced on the ONT GridION device (GRD-X5G003), aiming for ≥100X read depth. Guppy v6.4.2 was used to basecall and demultiplex the fast5-files with the super accurate basecalling model (see in silico L. pneumophila SBT approach were ONT sequenced using Kit9/R9.4.1 (n=39) or Kit12/R10.4 (n=7) and basecalled with Guppy v6.4.2 using the super accurate model  to perform read trimming, de novo assembly and quality control: TrimGalore v0.6.7  was used to remove adapter-contamination and low-quality reads, prior to assembly. We generated two sets of short-read assemblies : The first with Unicycler v0.5.0  was used to discard the worst 5% of read bases and any reads shorter than 1 kbp prior to assembly. We then generated long-read assemblies with three different tools: Unicycler v0.5.0 (which uses a miniasm v0.3-r179 + racon v1.5.0 pipeline), Flye v2.9 .For the long-reads, Filtlong v0.2.1  were analyzed using Sanger sequencing according to the standardised protocol of the ESCMID Study Group for Legionella Infections (ESGLI)   or the Shttps://github.com/tseemann/legsta) and/or with BLASTn v2.12.0+  against hybrid assemblies of these STs and viewed the bam files in IGV v2.15.2 , see section 3.3 for details.To identify both 33.1L. pneumophila genomes (n=233), there were 62 unique STs. Of these, 18 STs (29%) carried non-identical copies of mompS. We included a set of 35 isolates in this study; 27 isolates representing each of the 18 STs with non-identical mompS copies and 8 isolates (7 STs) with identical copies. Currently, to identify L. pneumophila SBT at the National Reference Laboratory for Legionella, isolates are subjected to Illumina short-read sequencing, followed by Unicycler assembly (which uses SPAdes), and legsta, which is an in silico SBT tool, to determine the ST. We have encountered three main challenges with this method: 1) erroneous calling of the mompS1 allele instead of mompS2 by legsta, 2) misassembly of the mompS genes when using Unicycler v0.4.8, and 3) failure to resolve the structure of the mompS-region with Unicycler v0.4.8 and v0.5.0.In our in-house sequence database of mompS gene. Upon repeated runs of legsta v0.5.1 using the same input assembly-files, the mompS2 allele called varied between the two alleles found in mompS1 and mompS2. This was the case for all genomes with non-identical mompS copies where both alleles were defined in the database. This issue was solved by examining the assembly graph in Bandage , Sanger sequencing always determined the mompS2 gene with allele 15, while the short-read assembly often determined the mompS1 and mompS2 genes as identical, either with allele 7 or 15. Further, the identified mompS2 allele sometimes varied between 7 and 15 when the same isolate was re-sequenced. We investigated this with read mapping, which showed that the two nucleotides were mapped to both mompS1 and mompS2 at different frequencies. Further, for some of the ST154 and ST574 isolates, the short-read assemblies contained an additional copy of the mompS gene, resulting in three identical mompS copies per isolate. This misalignment and misassembly of reads occurs because of the repetitiveness of the mompS-region and that the Illumina read lengths are shorter (≤300 bp) than the 352 bp part of the mompS2 gene that is used for allele determination  had identical mompS copies. Investigations of the assembly graphs showed that the mompS-region of these isolates consisted of several short contigs that did not span the mompS genes and the flanking regions, which are used to distinguish mompS2 from mompS1.The third challenge; failure to resolve the structure of the .4.8 see Table 1.We investigated whether the read length or read depth affected the results we observed with the short-read assemblers. The isolates were sequenced with different Illumina kits leading to paired-end read lengths of 2x300 bp, 2x250 or 2x150 bp see Table S1mompS2, which varied with different assemblers or versions of the same tools, indicates major challenges with using short-read sequencing to identify the mompS2 allele, and thus the ST, for several L. pneumophila isolates.In sum, the observed misassembly or untypeability of 3.2mompS-region  to see if mompS2 could be reliably identified using ONT-only reads. The 35 isolates were ONT sequenced, assembled and polished to create seven sets of assemblies for each isolate , and the mompS allele was sometimes incorrectly called in genomes with non-identical copies.To perform 3.3mompS1 and mompS2 alleles and the L. pneumophila ST from ONT-only and hybrid assemblies, we have developed the open source tool ONTmompS v2.0.0 (https://github.com/marithetland/ONTmompS). For each input assembly, the tool first identifies the mompS2 allele by distinguishing it from mompS1: BLASTn v2.12.0+ is used to query the assembly against a 987 bp reference sequence (see mompS tool (https://github.com/bioinfo-core-BGU/mompS) developed by mompS2 gene that are used for allele definition, and flanking regions, including the 430F upstream primer and the downstream primer 1116R. Next a pairwise Smith-Waterman local sequence alignment (EMBOSS v6.6.0.0)  of Klebsiella pneumoniae in Kleborate v2.3.1 (https://github.com/katholt/Kleborate) (mompS1 and mompS2 copies and 2) the ST together with the alleles of the seven genes in the scheme.To identify the ence see Figure 16.6.0.0)  is perfom mompS1 . The copeborate) . ONTmompmompS2 (and thus ST) for all Trycycler+Medaka and hybrid assemblies, in accordance with Sanger sequencing or EQA results for all 35 isolates.Using ONTmompS v2.0.0, we were able to identify mompS copies and were typeable with short-read sequencing, were also typeable with this ONT-only workflow.This analysis showed that all the isolates that were misassembled or untypeable when using short-read assembly methods were typeable with ONT-only assemblies when using Trycycler for assembly together with Medaka long-read polishing. The controls, i.e. the isolates that had identical L. pneumophila SBT, and potentially also from the Kit9/R9.4.1 chemistry/flow cells, we tested our workflow on an additional 46 ONT-sequenced isolates  are a reliable and effective solution for L. pneumophila SBT.To further test and confirm that ONT-only reads are sufficient for 4mompS2 from Illumina short-read sequences and have shown that ONT long-read sequences are a reliable alternative for determining mompS2 and the ST of L. pneumophila. Today, epidemiological investigations of L. pneumophila and associated disease or outbreaks are mainly done by Sanger SBT and/or WGS analyses. The challenges with identifying mompS2 due to non-identical copies of mompS and the short length of Illumina reads is a hindrance for routine use of short-reads in typing and surveillance. In 2015, a WGS-based cgMLST typing scheme for L. pneumophila was proposed, which showed high resolution of strains within the same ST/clonal complex from short-reads, but the challenge with typing due to non-identical mompS copies remained unsolved (https://github.com/bioinfo-core-BGU/mompS) was developed to solve the challenge by accepting only reads that also flanked the mompS2 gene and thus ensuring correct allele calls, and to ensure backwards compatibility with the Sanger SBT scheme . This in silico approach for L. pneumophila SBT takes assemblies as input and uses the L. pneumophila SBT database together with primer sequences to identify the correct alleles for the SBT scheme. For assemblies from short-reads, long-reads, and hybrid assemblies, we unfortunately experienced that this tool was sometimes unable to call alleles or called the incorrect mompS allele. Neither the mompS tool nor legsta have been maintained or updated for several years.In this study, we encountered several challenges when calling unsolved . In 2017T scheme . HoweverL. pneumophila genomes also showed that different assemblers or even different versions of the same assembler affected the typeability of the mompS2 allele, adding yet another layer of complexity to the challenge of identifying STs from short-reads. When taking together the challenges of resolving the mompS-region from short-reads and the issues with identifying the mompS2 allele with existing tools, it was clear that an alternative method was needed, which is why we explored using ONT long-reads and developed ONTmompS to perform in silico L. pneumophila SBT on ONT-only or hybrid assembled genomes.Our analyses of short-read mompS-region and to perform in silico L. pneumophila SBT on all 81 genomes that were tested . Both the Kit9/R9.4.1 and Kit12/R10.4 chemistry/flow cells with Guppy’s super accurate basecalling model were used. The assemblies that were created with Trycycler followed by long-read polishing with Medaka (Trycycler+Medaka) were assigned the same ST as with Sanger SBT, EQA or hybrid assemblies for all isolates where these results were available, and an ST was assigned when using ONTmompS in all of the tested genomes. With Trycycler-only, Flye+Medaka and Unicycler+Medaka, the STs of a few of the 35 genomes did not match with the Sanger/EQA or hybrid assemblies. There were no apparent quality issues with these genomes. We therefore recommend using Trycycler with Medaka long-read polishing for ONT-only assemblies for in silico SBT of L. pneumophila.With ONT-only reads and the ONTmompS tool, we were able to resolve the L. pneumophila. This is consistent with two recent studies of ONT sequencing: Bordetella pertussis from ONT-only assemblies from Kit12/R10.4 yielded results with comparable accuracy as from short-reads. Similarly, Klebsiella pneumoniae, but not sufficient for defining outbreak clusters. It was outside the scope of our study to investigate if the R10.4 ONT-only reads could be used for defining outbreak clusters of L. pneumophila. However, L. pneumophila). There was no difference in the typeability of the genomes that were ONT sequenced with Kit9/R9.4.1 and Kit12/R10.4, indicating that both chemistries/kits are appropriate for determining STs of By combining real-time basecalling and the ONT Kit12/R10.4, L. pneumophila SBT is important for the characterization of L. pneumophila isolates and for standardized comparison of results over time and between different laboratories (ratories . In sourratories . By utilratories .mompS2 and L. pneumophila ST. Our analyses show that ONT-only assemblies provide a cost- and time-efficient solution for determining L. pneumophila ST from WGS, where Illumina short-reads often fail to identify mompS2. For the best results, we recommend basecalling with Guppy using the super accurate basecalling model, assembly with Trycycler and polishing with Medaka, before identifying the ST with ONTmompS.To conclude, ONT-only sequencing is sufficient for identifying The datasets presented in this study are available online in the European Nucleotide Archive under accession numbers PRJEB58776 and PRJEB50383. Please see AK, MH, EB, and IL conceptualized the study. AK and EB performed genome sequencing. MH and MS developed ONTmompS. AK and AB analyzed Sanger and WGS results. IL provided resources and supervision. AK, MH and EB wrote the first draft of the manuscript. All authors contributed to data interpretation, reviewed and edited the manuscript, and have read and agreed to the published version of the manuscript.


Thermococcus sibiricus (TsA) with a mesophilic-like GSQ combination. In this study, the conjoint substitution of two residues adjacent to the substrate-binding Ser55 resulted in a significant increase in the activity of the double mutant, reaching 240% of the wild-type enzyme activity at the optimum temperature of 90 °C. The mesophilic-like GSQ combination in the rigid structure of the thermophilic L-ASNase appears to be more efficient in balancing substrate binding and conformational flexibility of the enzyme. Along with increased activity, the TsA D54G/T56Q double mutant exhibited enhanced cytotoxic activity against cancer cell lines with IC90 values from 2.8- to 7.4-fold lower than that of the wild-type enzyme.L-asparaginases (L-ASNases) of microbial origin are the mainstay of blood cancer treatment. Numerous attempts have been performed for genetic improvement of the main properties of these enzymes. The substrate-binding Ser residue is highly conserved in L-ASNases regardless of their origin or type. However, the residues adjacent to the substrate-binding Ser differ between mesophilic and thermophilic L-ASNases. Based on our suggestion that the triad, including substrate-binding Ser, either GSQ for meso-ASNase or DST for thermo-ASNase, is tuned for efficient substrate binding, we constructed a double mutant of thermophilic L-ASNase from  Among currently used enzymes, L-asparaginase (L-ASNase) remains one of the most important used in medicine, biosensors, and food industry. L-ASNase  catalyzes the hydrolysis of L-asparagine (L-Asn) to L-aspartic acid and ammonia ,3. CurreL-ASNase therapy is based on L-Asn starvation of susceptible tumor cells that are unable to synthesize their own L-Asn. By hydrolyzing extracellular L-Asn, L-ASNase leads to the death of lymphoblastic cells by apoptosis ,7.Relatively recently, the biotechnological area of L-ASNase application has expanded. In 2002, it was discovered that high contents of acrylamide are formed between reducing sugars and L-asparagine during high-temperature (over 120 °C) processing of starchy foods under low humidity in a non-enzymatic Maillard reaction .By hydrolyzing L-Asn, L-ASNase prevents the formation of carcinogenic acrylamide in the Maillard reaction . As aspaAnother field of the enzyme application is the development of biosensors. Biosensor systems based on L-ASNase allow to detect the level of L-Asn in medicine and in the food industry ,13.Thus, L-ASNase is an enzyme that is widely used in biotechnology under various operating conditions, in particular, in a wide range of temperatures. Most L-ASNases exhibit optimal activity at or near mesophilic temperatures (approximately 30–40 °C) and under mild operating conditions . ObviousDue to their superior performance, investigation of thermo-ASNases is of particular interest. Elucidation of the molecular mechanisms that determine the relationship between the activity, stability, and flexibility of thermo-ASNases is critical for both fundamental and applied research in view of their biotechnological significance.Thermococcus sibiricus (TsA) [Previously, we have characterized a new promising hyperthermophilic L-ASNase from the archaea us (TsA) . The enzErwinia carotovora and Erwinia chrysanthemi [Escherichia coli L-ASNase with improved activity by adapting a genetic algorithm of protein modeling in combination with molecular dynamics flexibility studies [Bacillus subtilis L-ASNase with improved thermostability was obtained [Various protein engineering approaches are used in an attempt to improve the properties of known L-ASNases. Based on the traditional directed evolution method, Kotzia and Labrou engineered mutants of mesophilic L-ASNases from santhemi . A thermsanthemi ,20. Enzy studies . The seqobtained . Based oobtained .Here, we report an attempt to improve TsA properties by the conjoint substitution of two so-called “special residues”. Based on available experimental data, amino acid residues that are highly conserved among thermo- but not mesophilic L-ASNases  can affect the thermoactivity and thermostability of thermo-ASNases .The single mutations had been previously found to decrease L-asparaginase activity of wild-type thermo-ASNases  or were A pairwise sequence comparison of thermophilic/mesophilic enzymes is an effective way to explore key amino acid substitutions affecting the activity and stability of enzymes evolutionarily adapted to different working temperatures.E. coli L-ASNase II (EcAII), used as a representative L-ASNase of mesophilic origin, revealed that in the specific reaction, Thr12, Ser58, and Thr89 of processed form  “clamp” and stabilize the substrate [Studies of ubstrate ,25,26,27ubstrate , while GE. coli enzyme by Gly 57 and Gln59, respectively , Thr53 corresponding to Thr56 of TsA was replaced by Gln found in EcAII at the same position. It was reported that the T53Q substitution affected PfA substrate affinity, but the effect of the mutation on PfA activity was not provided [Previously, in an attempt to improve the enzymatic properties of a low-activity hyperthermophilic enzyme from provided .T. sibiricus was absent, so the model of this enzyme was designed using method of homology modeling using the crystal structure of thermo-ASNase from Thermococcus kodakarensis (TkA) as a template. Thr56 of TsA is located at the bottom of the active site pocket and is involved in multiple interactions (E. coli asparaginase complex (pdb 3eca) into the model after spatial alignment of L-ASNase and the TsA model. It was shown that Thr86, Asp54, and Lys157 are also involved in the interaction with the substrate (product)  b.ST → GSQ.Analysis of the TsA model showed that the replacement of Thr56 with the corresponding Gln residue of EcAII can disrupt the H-bond network by shifting the position of the Gln amide group away from Asp87, but toward Asp54. Destruction of the H-bond network will increase the flexibility of the loop and improve the enzymatic properties of the thermo-ASNase. On the other hand, due to small distances, the appearance of bulky Gln instead of Thr in TsA can cause the Gln and Asp residues to move away from the substrate, resulting in a decreased efficiency of formation of the substrate–enzyme complex. To prevent this, it is preferable to reduce the size of the near located residues. Thus, if Thr is replaced with Gln, a simultaneous mesophilic-like substitution is needed to provide high specific activity of thermo-ASNase—DAdditionally, it was previously reported that the single D → G mutation reduces L-asparaginase activity of all thermo-ASNases studied due to impaired substrate holding by the catalytic Ser residue and adjacent Gly after Asp replacement .ST triad with the resulting GSQ combination may be beneficial in increasing the catalytic activity of TsA.Based on previous experimental data and homology modeling, the conjoint substitution in the highly conserved for thermo-ASNases DtsA_mod, which was previously developed for more efficient heterologous expression of the archaeal protein in E. coli cells [tsA_mod inserted into the pET-28a (+) vector was performed. The constructed plasmid was transformed into the host E. coli BL21 (DE3) for the expression of the D54G/T56Q mutant. The wild-type and mutant enzymes were purified by ion-exchange chromatography under the same experimental conditions with final yields shown in In an attempt to study the role of the conjoint substitution adjacent to the substrate-binding Ser, a TsA double mutant was developed. The mutant was constructed on the basis of the codon-optimized gene li cells . Site-diThe molecular weight of the purified D54G/T56Q mutant was ~37.5 kDa when analyzed with SDS-PAGE .The enzymatic properties of the double-mutant D54G/T56Q compared with the wild-type enzyme were investigated. According to our previous data, TsA is one of the most active thermo-ASNases, exhibiting high hydrolysis activity toward L-Asn . AnalysiSubstrate specificity experiments demonstrated that the D54G/T56Q mutant has lower glutaminase activity than the wild-type enzyme. The relative L-glutaminase activity was 5% for the mutant and 7% for wild-type TsA.m and Vmax of the mutant enzyme for substrate L-Asn were found to be 6 mM and 8170 µM/min, respectively. Under the same experimental conditions, the Km and Vmax of the wild-type thermo-ASNase TsA were estimated to be 3 mM and 4020 µM/min, respectively.The kinetic properties of the enzymes were assessed. The Km and Vmax values compared with the wild-type enzyme.Thus, for the double mutant, the mesophilic-like substitution in the active site caused a simultaneous increase in the specific activity and Kopt) of the enzyme  → Gly (meso-ASNase EcAII) or Thr (thermo-ASNase) → Gln (meso-ASNase EcAII)—impairs proper substrate binding. Nevertheless, restoring the entire mesophilic-like triad GST triad is “inverse” in thermo-ASNases compared to mesophilic GSQ relative the substrate-binding Ser. Thus, both previously used mutational strategies in this region to replace single Asp or Thr residue with their directly corresponding residues of EcAII are not entirely correct from the point of increasing activity.In EcAII, the downstream residue Gln59 assists the Ser58 residue in substrate binding. In thermo-ASNases, the upstream Asp residue adjacent to the substrate-binding Ser is supposed to play the same role. The puzzle is that, in terms of function, the DSQ residues involved in substrate binding and stabilization can lead to an increase in docking strength in thermo-ASNases. At high docking strength, the docking lifetime is longer than the time required for catalysis [In thermo-ASNase PfA, the T53Q mutant is reported to have an increased substrate affinity of 8.3 mM compared to 12.1 mM for the wild-type enzyme. At the same time, the substitution of conservative Thr53 correlates with a more than 2-fold loss in catalytic efficiency . The comatalysis .Pyrococcus yayanosii (PyA) and Thermococcus gammatolerans (TgA), the Asp → Gly replacement with the resulting “mixed” triad residues GST impaired substrate binding, increased flexibility around the binding Ser residue due to the deficiency of polar contacts [In turn, in thermo-ASNases contacts . DecreasAlthough single mesophilic-like substitutions of residues adjacent to the substrate-binding Ser—Thr→Gln and Asp→Gly reduced the catalytic efficiency or activity of thermo-ASNases, the simultaneous introduction of their combination in TsA increased the specific activity of the enzyme .SQ triad, fine-tuned to substrate binding in mesophilic EcAII, provided proper “clamping” and stabilizing of the substrate during the catalytic process in TsA thermo-ASNase. The increased flexibility of the structure around the active site may promote reorientation, substrate binding and increase the specific activity.The predicted model of TsA showed that the structure of the active site is rigid, stabilized by a network of H-bonds. The T56Q mutation disrupts this network, increasing the flexibility of the active site, but the appearance of a bulky glutamine side chain can affect the interaction of the substrate in the active site. This observation leads to the mutation of the second residue to increase the available volume to accommodate the glutamine side chain. Thus, the second mutation D54G was chosen. The possible conformation of residues in the active site of the double mutant is shown in Analysis of the mutant specific activity revealed that the conjoint substitution D54G/T56Q led to a significant increase in activity toward L-Asn—from 2066.1 U/mg for TsAwt to 5037.7 U/mg for the double mutant. In addition, a slight decrease in glutaminase activity was observed: from 7% for TsAwt to 5% for the mutant D54G/T56Q. Consistent with our data, in studies of EcAII, it was shown that Gly57 and Gln59 residues can modulate the substrate specificity of L-ASNase ,26,27.By analyzing the effect of temperature on the activity and stability of the double mutant, it was revealed that there was no shift in the optimum temperature, while the sensitivity to temperature changes and time-dependent loss of absolute enzyme activity increased.opt values were 30 °C and 25 °C lower than those of the wild-type enzymes, respectively  was previously obtained in our laboratory and used in the study [tsA were purchased from SibEnzyme . The expression strain E. coli BL21 (DE3) was purchased from Novagen .All chemicals used in the experiments were of analytical grade and purchased from Fluka Chemical Corp. , Serva , Paneco  or Reachem . The plasmid pET28a-TsA harboring gene he study . The enztsA_mod optimized for expression of archaeal L-ASNase TsA in E. coli cells and inserted into the pET-28a(+) vector was used as a template for PCR-based site-directed mutagenesis [Gene agenesis . The oliE. coli cells for expression. DNA sequencing was used to verify the mutations.DpnI-treated PCR products were transformed into competent g for 15 min. Eight grams of cooled biomass was suspended in 100 mL of buffer , destroyed by ultrasound, and purified as previously described [Expression and purification of the recombinant enzymes were performed as previously described with minor modifications . Recombiescribed .Protein concentration was determined using the method of Sedmak . SDS-PAGThe activity of L-ASNases was determined using direct Nesslerization ,44. The Determination of D54G/T56Q kinetic parameters was performed in glycine–NaOH buffer  containing 20–250 μM L-asparagine at 90 °C .Activity of D54G/T56Q was analyzed in the temperature range from 45 to 100 °C with 5 °C increments. Additionally, activity was measured at a physiological temperature of 37 °C. The mixture was assayed in glycine–NaOH buffer .Thermostability of TsA-D54G/T56Q was assayed by detecting the residual activity of the enzyme that had been preincubated at temperatures ranging from 60 to 90 °C with 10 °C increments at pH 9.3.The pH dependence of the mutant activity was analyzed by measuring the enzyme activity at different pH values at 90 °C in four buffer systems: sodium acetate , sodium phosphate , Tris–HCl buffer , and glycine–NaOH buffer .For the wild-type enzyme TsA, experiments were performed as previously described .Stability assay in the presence of 0–8.0 M urea was performed in glycine–NaOH buffer  for TsA-D54G/T56Q or Tris–HCl buffer  for TsA as previously described . The mea2+, Cu2+, Mg2+, Zn2+, Ca2+, Fe3+) and EDTA were added at a concentration of 10 mM. The enzyme activity was assayed at 90 °C and optimum pH values by adding L-asparagine and the corresponding metal ion(s) or EDTA. The measured activities were compared with the activity of the enzymes without metal ion or EDTA addition under the same conditions.To evaluate the effects of metal ions on enzyme activity, various cations  were used in the study. Human mammary gland adenocarcinoma Sk-Br-3, lung epithelial carcinoma A549, and chronic myelogenous leukemia K562 cell lines were used as cancer cells, whereas WI-38 normal human fibroblast were used as control non-cancer cells. Sk-Br-3, A549, and K562 cells were grown in RPMI-1640 medium, and WI-38 cells were grown in DMEM. Growth media were purchased from Gibco . All media were supplemented with 5% fetal bovine serum and 1% sodium pyruvate . The cells were grown at 5% CO2/95% air at 37 °C. Cells were tested for mycoplasma contamination before each experiment using the Mycoplasma Detection Kit PlasmoTest .4 cells per well, and incubated for 24 h. The enzyme was added to culture medium in the range of concentrations 1–100 U/mL and cells were incubated for 72 h. After that the solution of 3--2,5-diphenyltetrazolium bromine  at the concentration 5 mg/mL was added, and cells were incubated for 4 h more, followed by cell lysis with DMSO  and absorbance measurement at 570 nm. Non-treated cells were used as control. The viability of treated cells was expressed as a percentage relative to control. IC50 and IC90 values  were calculated from curve-fitting equations.Effect of the enzyme on cell growth and proliferation was examined via an MTT assay  by measu4 cells at each time point [To measure apoptosis, cells were incubated with 10 U/mL of the enzyme for 72 h in T-25 flask. Incubated cells were resuspended in PBS and labeled with annexin V-FITC and propidium iodide (PI) from a FITC Annexin V/Dead Cell Apoptosis kit  according to the standard manufecturer’s protocol. Flow cytometry was performed using a MACS Quant Analyzer 10  to count 5 × 10me point .The data from three parallel experiments were presented as the mean value ± standard error of mean. One-way analysis of variance (ANOVA) using Microsoft Excel (version 2016) was used for statistical analysis.t-test statistical analysis was used by Statistica software . Differences p ≤ 0.05 were considered significant. The results are presented as the mean ± standard error of the mean (SEM).In the measurement of cell viability and apoptosis induction, Student’s T. sibiricus was designed using SWISS-MODEL [https://swissmodel.expasy.org/, accessed on 10 April 2023). The structure of L-ASNase from T. kodakarensis was selected (PDB ID: 5ot0) as template, with 62.69% identity to TsA. The mutations were done using PyMol program. Rotamers were selected that did not overlap neighboring residues followed by structure optimization by minimization of energy. Tripos force field was used. Partial atomic charges were calculated using Gasteiger-Huckel method.The spatial structure of L-ASNase from SS-MODEL  (https:/


Bladder cancer (BC) is among the most common cancers diagnosed in men in the USA. The current gold standards for the diagnosis of BC are invasive or lack the sensitivity to correctly identify the disease.An aptamer-based screen analyzed the expression of 1317 proteins in BC compared to urology clinic controls. The top hits were subjected to systems biology analyses. Next, 30 urine proteins were ELISA-validated in an independent cohort of 68 subjects. Three of these proteins were next validated in an independent BC cohort of differing ethnicity.d-dimer displayed the highest accuracy (0.96) and sensitivity of 97%. Furthermore, 8 urine proteins significantly discriminated MIBC from NMIBC (AUC = 0.75–0.99), with IL-8 and IgA being the best performers. Urine IgA and fibronectin exhibited the highest specificity of 80% at fixed sensitivity for identifying advanced BC.Systems biology analysis implicated molecular functions related to the extracellular matrix, collagen, integrin, heparin, and transmembrane tyrosine kinase signaling in BC susceptibility, with HNF4A and NFKB1 emerging as key molecular regulators. STEM analysis of the dysregulated pathways implicated a functional role for the immune system, complement, and interleukins in BC disease progression. Of 21 urine proteins that discriminated BC from urology clinic controls (UC), urine d-dimer for BC, it may have a role in the initial diagnosis or detection of cancer recurrence. On the other hand, urine IL-8 and IgA may have the potential in identifying disease progression during patient follow-up. The use of these biomarkers for initial triage could have a significant impact as the current cystoscopy-based diagnostic and surveillance approach is costly and invasive when compared to a simple urine test.Given the high sensitivity (97%) of urine The online version contains supplementary material available at 10.1186/s12916-023-02813-x. Bladder cancer (BC) is the fourth most common cancer diagnosed in men in the USA . The incThe current gold standard for the diagnosis of BC is cystoscopy. However, cystoscopy is often associated with complications including pain, urinary tract infection, and hematuria. Urine cytology is also commonly used for the diagnosis and surveillance of BC. This non-invasive method involves the examination of cells collected from a urine specimen. Research has indicated a high specificity of 86%, but it is constrained by a low sensitivity of 48% . There iThe United States Food and Drug Administration (FDA) has approved 6 urinary assays to use in conjunction with cystoscopy for the surveillance and diagnosis of BC. These include BTA stat, BTA TRAK, NMP22 BladderChek Test, NMP22 ELISA, UroVysion, and uCyt . A meta-Given these metrics, there is a need for better biomarkers for BC. Urine biomarkers are promising as a non-invasive test for BC. Urine can be obtained non-invasively, is a readily available biological fluid, and is close to the site of pathology. This allows for repeated tests as deemed necessary for both diagnosis and potential monitoring of disease progression. Urine is also advantageous for potential cost-effective point-of-care tests. Emerging urine point-of-care tests may empower individuals to monitor their health from the comfort of their own homes .As opposed to previous studies in the field examining a handful of proteins selected based on their known properties, here, we report the first and largest use of a comprehensive aptamer-based proteomic screen of urine samples from 42 subjects. This platform has been successfully applied in biomarker screens of several other diseases –21. AddiInclusion and exclusion criteria: In all cohorts, the included bladder cancer patients were patients in whom the diagnosis was established by cystoscopy and pathology. Subjects with other malignancies were excluded. Urine samples for the initial aptamer-based screen were obtained from the University of Texas Southwestern Medical Center and Bioreclamation , Westbury, NY. The samples included 15 urology clinic controls (“UC”) and 27 bladder cancer subjects including 5 Ta (non-invasive papillary carcinoma), 4 Tis (flat carcinoma in situ), 9 T1 (tumor spread to connective tissue), 4 T2 (muscle-invasive bladder cancer), 3 T3, and 2 T4 BC. Of these, 18 subjects  were classified as non-muscle invasive bladder cancer (NMIBC) while 9 subjects (stage T2–T4) were classified as muscle invasive bladder cancer (MIBC). It should be stressed out that UC does not refer to urothelial carcinoma but for urology clinic controls. Additionally, unless stated otherwise, all bladder cancer subjects included in this study had urothelial cancer. For replication of the findings from the initial proteomic screen, the independent validation cohort for ELISA consisted of samples obtained from the University of Texas Southwestern Medical Center (“UTSW cohort”). These included 31 UC samples and 37 BC samples . Of these, 30 subjects  were classified as NMIBC while 7 subjects (stage T2–T4) were classified as MIBC. UC samples included patients investigated for hematuria but found not to have any urological cancers. Subject demographics, including age gender and ethnicity, and clinical information pertaining to these samples are detailed in Table The samples for the aptamer-based screen consisted of 15 urology controls and 27 bladder cancer subjects . These urine samples were screened using an aptamer-based screening platform (“SOMAScan”) manufactured by Somalogic, as detailed previously , 22. In Altogether, 34 proteins were initially selected for ELISA validation based on the aptamer-based screening. Commercially available ELISA kits were purchased, and preliminary testing was conducted. The protein, ELISA manufacturer, optimal urine sample dilution, reason for selection, and outcome of ELISA testing are listed in Additional file d-dimer in the other cohorts, an agglutination assay was used to assay d-dimer in the Chinese cohort. A d-dimer detection kit (latex immunity ratio turbidity method) from SEKISUI was used to quantitatively detect the concentration of d-dimer in the Chinese cohort. Briefly, the d-dimer in the sample reacts with the monoclonal antibody to the mouse anti-human d-dimer. This causes agglutination and increased turbidity. Following this, the concentration of d-dimer was determined by measuring the variation of turbidity with a spectrophotometer.As opposed to the ELISA used to assay U-test was used to identify the proteins that were significantly up- or downregulated among the subject groups. Statistical p-values were computed for each biomarker. To address multiple testing correction, q-values were calculated to adjust for the false discovery rate. Fold change values were also computed to determine the ratio of protein expression from diseased to control subjects (BC/UC) and MIBC to NMIBC subjects . ROC analyses including AUC, cutoff, sensitivity, and specificity values were computed with easyROC version 1.3.1 [U-test or a Kruskal–Wallis test with Dunn’s multiple comparison. Statistical p-values from analyses were computed.Aptamer-based screening data was subjected to hybridization, median normalization, and creatinine normalization as detailed previously . Furtheron 1.3.1 . The biop-value < 0.05 in the aptamer-based screen (BC versus UC) were used for analysis. The top 10 biological processes, molecular functions, and KEGG pathways were plotted using R. The packages used include readxl and ggplot2 . The size of the dots represents the count/hit number of genes belonging to the annotation term, and the color of the dots represent − log10FDR value.Gene Ontology (GO) and KEGG  functional enrichment analysis was completed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8 8 . The top 330 proteins with a Mann–Whitney p-value < 0.05) were created using Cytoscape version 3.9.0  using the stringApp. MCODE clustering was preformed to discern highly interconnected nodes in the network. The top 3 clusters are plotted with their associated Reactome pathways. The top transcription factor and signaling molecule regulator were identified for the top 330 proteins in the aptamer-based screening  using the iRegulon plugin available through Cytoscape. The color of each node corresponds to the fold change. Nodes with a fold change of less than 1 range in color from blue to purple while those with a fold change greater than 1 range from pink to red, when comparing BC to UC.Protein–protein interaction networks for the top 330 proteins in the aptamer-based screen  was used to generate statistical p-values. A 2D PCA plot was generated for the top 119 proteins  where the two first principal components are plotted. Subject groups are differentiated by color and/or shape. A correlation plot for the top 50 proteins was also generated . Correlation coefficients for all possible protein pairs were computed using Pearson’s correlation coefficients.The volcano, PCA, and correlation plots were created in R using the readr, readxl, gplots, ggplot2, ggplot.multistats, scatterplot3d, Hmisc, data.table, and corrplot packages. All 1317 proteins are represented in the volcano plot. The data was log-transformed, and a Mann–Whitney p-value < 0.05 and a fold change > 2) were analyzed. Hierarchical clustering was performed in R. Each row corresponds to the creatinine-normalized protein level measured, and each column represents a sample . Proteins that are above the mean value for each biomarker are shaded yellow. Proteins comparable to the mean are shaded black. Those below the mean are shaded blue.Heatmaps were generated from the aptamer-based screening assay in order to cluster proteins with similar expression profiles together. Proteins significantly elevated in BC when compared to urology controls (p < 0.05 and FC > 2) were used for analysis. The importance of each biomarker was measured using the GINI coefficient. The resulting top 10 potential urine biomarkers identified by random forest classification are plotted.Random forest analysis, a supervised machine learning algorithm, was conducted for the purpose of identifying the relative importance of biomarker candidates in disease discrimination. The randomForest R package and the top 93 proteins identified from the aptamer-based screen  subjects. The urine levels of 21 protein biomarkers, features , and disease status (BC vs UC) were examined. The network was constructed in an unsupervised manner with the EQ algorithm. The size of each node was determined using node force and is proportional to its impact on the other nodes in the network. The arcs that interconnect the nodes were determined with Pearson’s correlation coefficient. The interconnections between nodes represent the dependencies among the variables including the correlation coefficient between nodes. The thickness of the arcs is proportional to Pearson’s correlation coefficient.p-value < 0.05) were averaged across the subject groups analyzed. The number in the upper left-hand corner of each box is representative of the number of genes in each cluster. The number in the lower left-hand corner is the p-value significance of the number of proteins assigned to the cluster versus what was expected based on permutation testing. Protein expression profiles through UC and progressive BC stages, Ta-Tis, T1, and T2–T4 are plotted. Statistically significant profiles that are similar form a cluster and are shaded the same color. A total of 50 cluster patterns were generated by STEM analysis, of which only the statistically significant clusters that exhibited a progressive increase or decrease of urine biomarkers across BC stages are plotted. The associated Gene Ontology (GO) enrichment analysis for profiles of proteins with the same expression patterns was determined. The Reactome pathways associated with each significant cluster were identified through Cytoscape and the functional enrichment tool.Short Time-series Expression Miner (STEM) version 1.3.13 was utilized for clustering, comparing, and visualizing protein expression data from the aptamer-based screen across bladder cancer  and urology controls (UC = 15). Protein expression among the top 330 proteins  were identified using the predictive projection feature selection technique executed with the projpred package in R (version 4.0.3.) [The protein level of each protein assayed by ELISA and the age of patients were standardized to have a mean of zero and a standard deviation of one, after applying log 4.0.3.) –26. Mode 4.0.3.) , 28 was  4.0.3.) . The selU-test was used to identify proteins that were significantly up- or downregulated among BC vs UC, resulting in 330 proteins. After multiple testing correction, 119 of these 330 proteins had a q-value < 0.05. The 330 proteins found to be statistically significantly different (both up- and downregulated proteins) in BC were subjected to functional pathway enrichment.An overview of the study flow is depicted in Additional file Functional pathway enrichment was performed using Gene Ontology analysis. The top 10 Gene Ontology biological processes identified from the differentially expressed urine proteins in BC can be found in Fig. Following this, protein–protein interaction networks were created with Cytoscape using the 330 differentially expressed proteins in BC to determine their interactions with one another. MCODE clustering was performed to identify highly interconnected clusters Fig. D. Node cp-value < 0.05) at a fold change of > 2, as depicted by the volcano plot in Fig. p-value < 0.001 and a fold change of > 5  were used as input for principal component analysis (PCA), an unsupervised machine learning algorithm, which successfully discriminated BC patients from the urology clinic controls  identified the 10 most discriminatory urine proteins with the greatest impact on distinguishing BC from UC  versus biological significance (x-axis). With this comparison, 8 urine proteins were found to be elevated in MIBC stages compared to NMIBC , as represented by the blue dots. Principal component analysis (PCA) demonstrated that the differentially expressed proteins in BC were successful in discriminating BC patients with more advanced disease (MIBC), from the urology clinic controls  with 93 upregulated at a fold change of 2, and 147 upregulated at a fold change of 1. In contrast, 8 proteins were identified as being significantly elevated with a fold change of 2 or greater, and 12 proteins obtained a fold change greater than 1 in MIBC compared to NMIBC . Four urine proteins overlapped between these two comparisons with a fold change greater than 1. These four overlapping proteins included urinary Elastase, S100A12, p53, and Kallikrein 6.A Venn diagram representation of the significantly elevated proteins identified through the aptamer-based screen is shown in Fig. Next, to identify urine proteins and functional pathways that progressively increased or decreased with the worsening BC stage, Short Time-series Expression Miner (STEM) analysis was executed. This analysis identified 2 distinct clusters of proteins that progressively increased with BC stage , with urine properdin and MMP-1 being close behind. As a sensitivity analysis, a bootstrap logistic regression model was used to derive optimism-corrected performance metrics and to ascertain the robustness of each urine protein for distinguishing BC from urology control. This method, which is more accurate for small sample sizes, yielded similar results, as listed in Additional file These 30 proteins were assayed by ELISA in a cohort of 68 urine samples, drawn from 31 UC, and 37 BC patients, comprising 10 Ta, 10 Tis, 10 T1, and 7 T2–T4 patients, referred to in this manuscript as the UTSW cohort  was also compared to NMIBC . The results of the 30 ELISAs for the MIBC vs NMIBC stage comparisons are detailed in Fig. d-dimer, MMP-1, Apolipoprotein A1, Proteinase 3, and Apolipoprotein L1, with an AUC of 0.95, a sensitivity value of 0.89, and a specificity value of 0.87. Not surprisingly, the top 3 proteins in panel 1  also ranked as the best single-marker performers, based on their individual AUC values  and α2-macroglobulin also exercised a large impact on other nodes in this network. Thus, this independent machine learning algorithm re-affirms the diagnostic potential of Apolipoprotein A1, d-dimer, and IL-8 in BC.We next subjected all 21 proteins that showed significant ROC AUC values in discriminating BC from UC and clinical diagnosis to an unsupervised Bayesian network analysis. This analysis uses probability distributions to represent the inter-dependencies between all variables in a model and how they relate to one another. Figure d-dimer, and Elastase which were among the best-performing markers in the first independent validation. Their associated creatinine normalized protein values are displayed in Fig. A secondary validation of upregulated urine proteins in BC vs UC was conducted in subjects of Chinese ethnicity. Three proteins were further assayed using ELISA in a cohort consisting of 77 UC and 91 BC patients  in the urine. A meta-analysis of BTA stat reported a specificity of 67% and a sensitivity of 75% after reviewing 13 studies . The senAs opposed to studies looking at an isolated protein in the urine, a few screens have been reported where multiple proteins were examined simultaneously. Summarized below are a couple of studies documenting biomarkers with both sensitivity and specificity values greater than or equal to 85%. Goodison et al. performed a validation study for the urinary concentrations of 14 proteins  using an ELISA . An 8-biLow-grade BC has a high recurrence rate; therefore, identifying biomarkers for the surveillance of BC is essential for the potential clinical management of the disease. Rosser et al. identified 10 biomarkers  using ELISA for monitoring urine for recurrent BC. The complete panel achieved an AUC of 0.90, a sensitivity of 79%, and a specificity of 88% . De PaolThere are several reasons to discriminate patients with bladder cancer from benign conditions. In patients with hematuria, it would be helpful to identify who needs cystoscopic evaluation which is invasive. Given that urine proteins are easily measurable and are compatible with point-of-care monitoring, a quick urine test could dramatically impact triage and workflow in urology outpatient clinics. Likewise, in bladder cancer surveillance, a reliable urine biomarker can help determine if cancer (like CIS) was missed or to avoid cystoscopy in marker-negative patients. Similarly, urine biomarkers that can reliably distinguish MIBC from NMIBC can inform us as to who has the more aggressive disease. When used as a routine point-of-care test (either at home or at outpatient visits), these urinary biomarkers may facilitate earlier identification of aggressive disease and design of tailored therapy.d-dimer, Apolipoprotein A1, MMP-1, Properdin, Calgranulin B) significantly discriminate BC from UC with AUC values from 0.85 to 0.96 . As a single biomarker, urine d-dimer was able to discriminate BC from UC with 96% accuracy . Likewise, several urine proteins  significantly discriminate MIBC from NMIBC with AUC 0.84–0.99 . Interestingly, several of the proteins described above have been documented to be elevated in bladder cancer tissue (at the RNA or protein level) and/or implicated in tumor biology at some level, as summarized in Additional file d-dimer [d-dimer is a specific cleavage product of fibrin and a symbol of hyperfibrinolysis [d-dimer, are abnormal in cancer patients [d-dimer levels show a significant ability to differentiate BC from UC (AUC = 0.96) (p < 0.0001). After correcting for patient demographics, urine d-dimer is still eligible for inclusion within the 5-biomarker panel for best distinguishing BC from UC. Perhaps most impressive is the observation that urine d-dimer demonstrates a high sensitivity for the detection of BC (95%), and at a fixed specificity of 0.8, it can achieve a sensitivity of 0.97. Hence, as a single biomarker, urine d-dimer outperforms current FDA-approved biomarkers and competing biomarkers in the research literature as a sensitive biomarker for BC detection.The present work represents the first attempt to screen > 1000 urine proteins for urine biomarker candidates in BC, using a relatively novel aptamer-based screen. Systems biology analysis implicated molecular functions related to the extracellular matrix, collagen, integrin, heparin, and transmembrane tyrosine kinase signaling in BC susceptibility, with HNF4A and NFKB1 being key regulators. STEM analysis of the dysregulated pathways implicated a functional role for the immune system, complement, and interleukins in BC disease progression Fig. D. This s d-dimer , Apolipo d-dimer , 36, Apo d-dimer , 38, com d-dimer , Fibrone d-dimer –43, Fico d-dimer –49, IgA  d-dimer , MMP-1 [ d-dimer , 52, Pro d-dimer . A summainolysis . It is tinolysis . Previoupatients . In the Apolipoproteins (Apolipoprotein A1 and Apolipoprotein L1) are proteins known to interact with the lipids of the lipoprotein core and also the aqueous environment of the plasma. Apolipoprotein A1 is the primary protein component of high-density lipoprotein while Apolipoprotein L1 is a minor component. Previous studies have validated Apolipoprotein A1 as a novel urinary biomarker for BC , 36. In After adjusting for demographics, this protein ranked within a 5-marker panel for distinguishing BC from UC. Similarly, Apolipoprotein L1 also ranked within the 5-marker panel for distinguishing BC from UC and MIBC from NMIBC.Calgranulin B (S100A9) is a zinc- and calcium-binding protein that plays a prominent role in regulating inflammatory and immune responses. Several S100 proteins, including S100A9, have received attention regarding their possible role in tumor development and progression and studies report an increased expression in a variety of tumors, including ovarian, colon, gastric, and prostate cancer . Increasp < 0.0001) in discriminating BC from UC. These biomarker findings are consistent with the observation that changes in complement activation constitute one of the major pathways that predict BC disease progression, based on STEM analysis .Fibronectin is a glycoprotein component of the extracellular matrix. Tumor cells can attach to fibronectin via integrins or other cell surface receptors . Its effp < 0.0001), although its specificity was modest at a fixed sensitivity of 80%. Taken together with a wealth of supporting literature, this marker has the potential to be a monitoring tool for BC disease progression and warrants further analysis in this context.IL-8 is a proinflammatory CXC chemokine. It has previously been associated with the promotion of neutrophil chemotaxis and degranulation . Increasp < 0.0001). Overall, IgA performed 2nd best out of a total of 30 urinary markers validated for this comparison. Of particular note, urine IgA exhibited the highest specificity of 80% for MIBC, at 80% sensitivity, out-performing IL-8.IgA is an immunoglobulin and is often the first line of defense in the resistance against infections, particularly in mucosal tissues. A correlation of intra-tumor IgA1 and poor overall survival in BC patients has been identified in a previous study . Howeverp < 0.0001). This protein was also included in a 5-marker panel for distinguishing BC from UC. At the mechanistic level, one can envision tissue matrix remodeling as an important pre-requisite for cancer progression.Matrix metalloproteinases (MMP) are a group of zinc-dependent proteolytic enzymes. Their role involves remodeling of the extracellular matrix. Many studies have evaluated the levels of MMPs in cancer patients and have reported the vital roles of some MMPs as potential diagnostic and prognostic biomarkers in tumorigenesis . The curd-dimer, Apolipoprotein A1, MMP-1, Properdin, and Calgranulin B were identified as the most discriminatory urine markers in distinguishing BC from UC. Given that urine d-dimer has 97% sensitivity (at 80% specificity) for detecting BC, it may have a role in the initial diagnosis of BC, or for the detection of BC recurrence during surveillance follow-up. Urine Apolipoprotein A1, Properdin, and MMP-1 are the next best-performing biomarkers in this respect. On the other hand, urine IL-8 and IgA may have the potential in identifying disease progression during BC patient follow-up.There is a need for these findings to be validated in additional cohorts. However, the urine proteins reported in this research exhibit great potential for use in a clinical setting. d-dimer, MMP-1, Apolipoprotein A1, Proteinase 3, and Apolipoprotein L1 need to be validated independently and in multi-marker panels in additional cross-sectional and longitudinal cohorts to confirm if they are superior to current FDA-approved markers for BC detection. Urine IL-8, Ficolin-3, Apolipoprotein L1, Properdin, and Proteinase 3 also need to be validated both independently and as a multi-marker panel in future cross-sectional and longitudinal cohorts to confirm if they are good indicators for bladder cancer disease progression. These novel urine biomarkers for BC also warrant systemic testing to assess their utility in BC surveillance and in predicting or monitoring response to treatment.Several aspects of this study could be improved. Our study is limited by the relatively small sample size and the imbalance between the number of patients with NMIBC and MIBC. Because of these limitations, the generalizability of the study’s findings warrants caution. This calls for future investigation in larger patient populations. Additionally, a larger sample size could uncover markers that are less discriminatory. Given that the current study only pursued the validation of 34 urine protein biomarkers, a larger number of additional proteins found to be significant could be assessed for validation in future studies. Urine Additional file 1: Fig. S1. Consort diagram outlining the flow of the study. Fig. S2. Principal component analyses of the SOMAscan results. Fig. S3. Correlation analysis of the top 93 proteins elevated in BC urine. Fig. S4. Box plot expression profiles of the 30 ELISA validated proteins (BC vs UC). Fig. S5. Box plot expression profiles of the 30 ELISA validated proteins (MIBC vs NMIBC). Table S1. Demographic and clinical information of the secondary validation cohort of Chinese ethnicity. Table S2. ELISA validation kits selected for 34 proteins. Table S3. Single marker AUC analysis for the comparison of BC vs UC. Table S4. Single marker AUC analysis for the comparison of MIBC vs NMIBC. Table S5. Literature and public database profiles of shortlisted urine proteins. Table S6. Literature profiles of the outstanding proteins in urine and serum. Table S7. Literature profiles of the outstanding proteins in tissues.


There is increasing evidence for an autoimmune aetiology in post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). SARS-CoV-2 has now become the main trigger for ME/CFS. We have already conducted two small proof-of-concept studies on IgG depletion by immunoadsorption (IA) in post-infectious ME/CFS, which showed efficacy in most patients. This observational study aims to evaluate the efficacy of IA in patients with post-COVID-19 ME/CFS. The primary objective was to assess the improvement in functional ability. Due to the urgency of finding therapies for post-COVID-Syndrome (PCS), we report here the interim results of the first ten patients, with seven responders defined by an increase of between 10 and 35 points in the Short-Form 36 Physical Function (SF36-PF) at week four after IA. The results of this observational study will provide the basis for patient selection for a randomised controlled trial (RCT), including sham apheresis, and for an RCT combining IA with B-cell depletion therapy. Trial registration number: NCT05629988. After a mild-to-moderate SARS-CoV-2 infection, approximately 5–10% of patients develop long-lasting symptoms that can be attributed to different conditions and symptom complexes, referred to as Post-COVID-19 Condition or Syndrome (PCS) . About hWe previously conducted a primary observational study to investigate the effect of immunoadsorption (IA) in patients with infection-triggered ME/CFS with elevated ADRB2 AABs. We observed a rapid improvement of symptoms with both short and long-term responses in seven out of ten patients . Given tIA is an apheresis technique used to remove immunoglobulins from a patient’s plasma. Plasma is passed through an absorber that can selectively bind immunoglobulin G (IgG) or all immunoglobulins . The absDue to the urgency of finding therapies, we present here an interim analysis of the results from the first 10 patients in our prospective observational IA study of patients with SARS-CoV-2-triggered ME/CFS. Patients with elevated ADRB2 AABs were selected based on the association of the levels with symptom severity . This stPatients were diagnosed with ME/CFS based on the 2003 CCC for ME/CFS. Patients were recruited from October 2022 to February 2023 at the Charité Fatigue Centre at the Institute of Medical Immunology, Charité Berlin. Inclusion criteria required having a positive PCR or antigen test for COVID-19 at disease onset and elevated ADRB2 AABs. Other relevant conditions that could cause PCS or fatigue were excluded.® columns designed for the specific removal of human lambda and kappa chains, including IgG (subclasses IgG1–IgG4), IgA, IgM, and IgE . The IA was performed within the approved use. Five sessions of IA treatments were carried out over a period of ten days, with a maximum of two days in between treatments. Two further IAs will be offered to responding patients who deteriorate again. We conducted an observational study to assess the effect of IA on physical disability, symptom severity, immunoglobulin, and antibody levels. The study was approved by the Ethics Committee of Charité Universitätsmedizin Berlin in accordance with the 1964 Declaration of Helsinki and its subsequent amendments. All patients provided written informed consent. Immunoadsorption was performed using TheraSorbIgG, IgA, and IgM levels were measured before IA, before the 5th IA, and four weeks after the first IA. Autoantibodies were measured before IA, after four IAs, and four weeks after the first IA. Antibodies against ADRB2 AABs were determined by CellTrend GmbH, Luckenwalde, Germany, using ELISA technology. Pre- and post-treatment samples were analysed in the same assay run. The upper normal levels of autoantibodies were determined based on validation studies of a healthy control group and defined as being larger than the 90th percentile of a healthy control group (14 U/L).The primary endpoint was to assess the effect of IA on physical function four weeks after IA by the Short-Form 36 version 2 questionnaire (SF 36), specifically the Physical Function (PF) domain. It has been shown that an increase of at least 10 points in the SF-36 PF scale, which ranges from 0 to 100 and where higher scores indicate better health, indicates a clinically relevant improvement (“a little better”) and an increase of 20 points, a greater clinical improvement (“much better”) . TherefoStudy data were collected and managed using REDCap electronic data capture tools hosted at Charité—Universitätsmedizin Berlin ,27. p-value of <0.05 was considered statistically significant. Statistical data analyses were conducted using GraphPad Prism 9.5.1, © 2023 GraphPad Software . Nonparametric statistical methods were used. Continuous variables were presented as median and interquartile range (IQR). Comparisons of different time points of two dependent groups were done using the Wilcoxon matched-paired signed-rank test. A two-tailed All patients were diagnosed with post-COVID-19 ME/CFS with a disease duration of nine to 32 months at study inclusion. Age ranged between 33 and 59 years, with six patients being female and four patients being male. Functional disability assessed by the Bell score ranged from 20 to 40, where a score of 100 represents the absence of functional disability, and the SF36-PF score ranged from five to 45, with 100 being the highest possible score. Patient characteristics are presented in In all patients, the total IgG levels were within the normal range (median 11.14 g/L) prior to the first IA and decreased to a median of 2.29 g/L (range 0.57–3.23) after four days of IA. IgA and IgM were within the normal range before IA (median 1.89 g/L (IgA) and 1.19 g/L (IgM)) and decreased to a median of 0.48 g/L and 0.27 g/L, respectively. Four weeks after the first IA, the levels of IgG, IgA, and IgM increased again to a median of 6.64 g/L (IgG), 1.53 g/L (IgA), and 0.77 g/L/(IgM), but they remained significantly lower compared to pretreatment A.ADRB2 AABs decreased in parallel with the immunoglobulin levels, going from a median of 26.2 U/mL (IgG)/1.8 U/mL (IgA)/2.8 U/mL (IgM) to a median of 7.7 U/mL/0.7 U/mL/1.2 U/mL after four IAs. They then increased again to a median of 21.2 U/mL (IgG)/1.7 U/mL (IgA)/2.4 U/mL (IgM) B. HoweveThe SF36-PF score, defined as the primary outcome parameter, ranged from five to 45 at the baseline (median 25). Seven patients reported an increase in SF36-PF by 10 to 45 points at week 4 after IA (to median 42.5), as shown in As depicted in p < 0.05), while improvements in the cognitive score and headache did not reach statistical significance. However, only four of the seven responders reported headaches before IA. Responders also described improvements in the core symptoms of pain, cognition, and immunological symptoms. Several patients reported an initial worsening of the fatigue alongside PEM, which they attributed to the overall strenuous process of the IA treatment. Fatigue scores assessed by the FSS showed no significant change .The scheduled therapy of 5 days of IA within 10 days could be carried out in all patients in an outpatient setting and took between 4.5 to 9 h. It could be performed with a peripheral venous catheter in six of the ten patients. Four patients needed a central catheter. The IA treatment, placement of the peripheral or central catheter, and daily travelling were rather stressful and several patients reported the triggering of PEM during the therapy. No further side-effects occurred. To ameliorate the procedure, we paid attention to good hydration and the minimisation of physical and mental stress as far as possible. Lorazepam for up to three days was offered as a supportive therapy.This interim report on the first 10 patients enrolled in our IA observational study provides first evidence that IA can improve physical function and symptoms in a subset of patients with ME/CFS following SARS-CoV-2 infection. PCS is a complex condition with immune  and non-AAB levels significantly decreased after IA in all patients, both responders and non-responders, and increased again after 4 weeks. There was no correlation between the AAB levels and efficacy, and patients with symptom improvement at week four showed a similar recurrence of AAB levels. Thus, mechanisms other than mere AAB depletion most likely account for the improvement in a subset of patients. Among these is the apoptosis of AAB-producing B cells. B cell phenotyping in our previous study provided the first evidence for the effect of IA on memory B cells . GPCR AATaken together, the first data from our study provide evidence that IA has efficacy in a subset of patients and, thus, AABs play an important role in the pathomechanism of SARS-CoV-2-triggered ME/CFS. Limitations of our study include the low number of patients having completed therapy so far and the non-controlled treatment. These results are, however, the basis for recruiting patients with SARS-CoV-2-triggered ME/CFS into an IA RCT with sham apheresis and an RCT combining IA with consecutive B-cell depletion. Further, repeat IA will be performed in this observational trial in responding patients who deteriorate again to learn if this can lead to longer remissions.


A. baumannii remains largely unclear. In this study, we selected a tigecycline-resistant and a tigecycline-susceptible strain from a tigecycline-susceptible and a resistant strain, respectively. Proteomic and genomic analyses were performed to elucidate the variations associated with tigecycline resistance. Our study showed proteins associated with efflux pump, biofilm formation, iron acquisition, stress response, and metabolic ability are upregulated in tigecycline resistant strains, and efflux pump should be the key mechanism for tigecycline resistance. By genomic analysis, we found several changes in the genome that can explain the increased level of efflux pump, including the loss of the global negative regulator hns in the plasmid and the disruption of the hns gene and acrR gene on the chromosome by the insertion of IS5. Collectively, we not only revealed the phenomenon that the efflux pump is mainly responsible for tigecycline resistance, but also highlighted the mechanism at the genomic level, which will help in understanding the resistance mechanism in detail and provide clues for the treatment of clinical multiple drug-resistant A. baumannii.The mechanism of tigecycline resistance in  A. baumannii increased dramatically. At present, A. baumannii has developed resistance to almost all commonly used antibiotics and has been listed as one of the six antimicrobial-resistant ESKAPE  pathogens by the World Health Organization [A. baumannii infection; however, outbreaks of infections caused by carbapenem-resistant strains have already been reported since the early 1990s [A. baumannii up until now.Hospital-acquired infections caused by multi-antibiotic-resistant gram-negative bacteria have become an extremely serious problem in recent decades. In the late 1970s, due to the abuse of broad-spectrum antibiotics and the extensive use of antibiotics in agriculture and animal husbandry, the antibiotic resistance of ly 1990s , making A. baumannii appeared, and the incidence of infections caused by tigecycline-resistant strains has certainly increased in recent years [Tigecycline, the third generation of tetracycline antibiotics , is a dent years .A. baumannii to tigecycline resistance is barely understood. Some studies using real-time reverse transcriptase PCR to correlate antimicrobial resistance with the expression of genes encoding efflux systems highlighted that overexpression of the AdeABC system was significantly associated with resistance to tigecycline [A. baumannii, but other resistance mechanisms may also be implicated [tet(X3) and tet(X5) which encode flavin-dependent monooxygenase, can mediate tigecycline resistance [trm gene encoding S-adenosyl-L-methionine-dependent methyltransferase and the plsc gene encoding 1-acyl-sn-glycero-3-phosphoacyltransferase can also result in increased tigecycline resistance [arp gene encoding peptidase C13 family can enhance cell membrane permeability and reduce bacterial resistance to tigecycline [A. baumannii, and further investigations are warranted so that effective treatment of MDR A. baumannii could be guided by validated in vitro data. In this study, we reversed the tigecycline-resistance in two strains by sub-minimum inhibitory concentration of tigecycline and high temperature, respectively, and comprehensively compared the proteomics and genomic differences of the strains before and after selection. We found variations both at the genomic and proteomic levels that were associated with tigecycline resistance. Our results will provide important clues for better understanding the mechanism of tigecycline resistance in A. baumannii.At present, the mechanism of ecycline ,7,8. Othplicated ,10,11. Wsistance . In addisistance ,14, and ecycline . All theTo better understand the mechanism of tigecycline resistance, we intended to select a tigecycline-resistant strain from the parent tigecycline-susceptible strain and then find the changes both at the genome and proteome. We incubated a tigecycline-susceptible strain 17978S with successive sub-inhibitory concentrations of tigecycline, and a strain 17978R with a tigecycline-resistant phenotype was successfully selected. The MIC to tigecycline of 17978R increased to 128 μg/mL compared with 0.5 μg/mL of 17978S. For comparison, we also incubated a tigecycline-resistant strain A54R at 42 °C, and a strain A54S with a tigecycline-susceptible phenotype was selected. The MIC of A54S reduced to 1 μg/ mL compared with 8 μg/mL of A54R. All selected strains were sub-cultured for 15 generations (days) under non-tigecycline conditions, and the tigecycline MICs did not change after passaging, suggesting that the tigecycline resistance after selection is stable. It is worth noting that the selected tigecycline-susceptible strain A54S could still recover its resistance to tigecycline with a recovery rate of approximately 0.0000015 ± 0.0000007.We first focused our attention on tigecycline resistance-related proteins in the two pairs of strains. Thirty-one different proteins were identified between 17978S and 17978R, of which twenty-six were up-regulated and five were down-regulated in 17978R. Twenty-five differential proteins were identified between A54R and A54S, of which eighteen were up-regulated and seven were down-regulated in A54R . The up-Since efflux pump-related proteins were up-regulated both in the tigecycline-resistant strains 17978R and A54R, and previous studies also showed efflux pumps play important roles in antibiotic resistance , we trieIn order to further explore the tigecycline resistance mechanism, we conducted third-generation whole genome sequencing analysis on the two pairs of strains to study the genetic differences before and after selection. The information on the whole genome sequencing is shown in hns gene was found in this plasmid. No genes directly related to antibiotic resistance were found . XH386 is an MDR strain isolated from a pediatric hospital in China [17978S and A54R both contain two plasmids, with sizes of 148,955 bp (17978S-plasmid 1) and 24,822 bp (17978S-plasmid 2) in 17978S, and 110,967 bp (A54R-plasmid 1) and 69,098 bp (A54R-plasmid 2), respectively. Compared with 17978S, the large plasmid (17978S-plasmid 1) was lost in the selected strain 17978R. The plasmid 17978S-plasmid 1 contains 156 encoding genes, 105 of them encoding hypothetical proteins; the remaining genes encode proteins involved in plasmid conjugation and replication, growth metabolism, virulence, and transposition. It is worth noting that a re found A. Comparre found B. The A5in China , DNA syntet(X) [tet(X) gene in all the plasmids in 17978R and A54R, suggesting that the tigecycline resistance is not mediated by tet(X)-containing plasmids in A54R and 17978R.It was reported that plasmid-mediated resistance to tigecycline is usually due to the flavin-dependent mono-oxygenase-coding gene tet(X) . We analacrR, which encodes a transcriptional regulator for AcrAB. In A54R, the genes contain armA, msrE, and mphG, which are associated with aminoglycoside and macrolide antibiotic resistance.Comparative genomic analysis with MAUVE showed distinct variations between 17978S, 17978R, A54R, and A54S. The genome has high collinearity between 17978S and 17978R, and no large sequence rearrangements were found . In contA. baumannii ATCC 19606 to tigecycline, minocycline, and doxycycline [ata, ahpF, and tuf. Ata is an important virulence factor for many gram-negative bacteria, which can help mediate adhesion and invasion, induce apoptosis, and cause host diseases.Next, we analyzed the single nucleotide variations (SNV) in the strains . There aycycline . In thissul2 than 17978R, which is involved in the resistance to sulfonamide antibiotics [armA, msrE, mphG, sul2, and emrE are located on the genome island GI-5 of A54R , suggesting the involvement of H-NS in regulating the tigecycline resistance of A. baumannii. Then, we sought to figure out whether the insertion of IS5 in the hns gene was a random or fixed event when A. baumannii was put under the pressure of sub-MIC tigecycline. We performed the selection experiment again and checked the hns gene in the strains during the selection process and found no IS5 insertion into the hns gene at any time point during the selection process . The loss of this large plasmid can activate T6SS, but also can reduce bacterial antibiotic resistance. In this study, both 17978S and A54R lost a large plasmid during the selection process. However, no antibiotic related genes were found in the plasmids. We transformed the plasmid of A54R into A54S or E. coli DH5α but failed to obtain tigecycline-resistant transformants, at the same time, A54S has a low probability of recovering its resistance, both suggesting that the plasmids in A54R are probably not the element that directly mediates tigecycline resistance. The lost plasmids encode a variety of proteins, including proteins related to plasmid conjugation and replication, toxin-related proteins, metabolism-related proteins, and DNA-related active proteins. These suggested that to adapt to the sub-MIC tigecycline environment or high temperature, A. baumannii has to discard genes that are not necessary for survival to regulate their basal life activities and focus on resisting external pressure.Plasmids are important elements mediating the acquisition and loss of genes. Weber et al.  found thA. baumannii may acquire tigecycline resistance by single nucleotide variation on some genes, such as ACX60_03215 which encodes a SAM-dependent methyltransferase. At the same time, both in 17978S and A54R, we found large fragment deletions that contain multi-drug resistance-related genes, suggesting that horizontal gene transfer should play an important role in the tigecycline resistance of A.baumannii.By whole genome analysis, we found that the chromosome of 17978R selected by sub-MIC has high collinearity with 17978S, and there were no large fragment changes such as translocations, while in A54R/S a large fragment inversion appeared. This suggests that the selection pressure of sub-MIC selection on the genome of the strain is relatively mild, and the high temperature selection will make the strain adjust its genome to adapt to this strong survival pressure, so as to survive better. At the genomic level, we found A. baumannii was altered under sub-MIC tigecycline stress. IS elements can alter the antibiotic resistance of bacteria by inserting into the upstream of the carbapenemase and cephalosporinase resistance genes and thus increasing the expression of these genes, resulting in higher levels of antibiotic resistance properties [hns in 17978R, and confirmed that this phenomenon occurs randomly, which is conducive to the random mutation of bacteria and helps the development of biodiversity.Previous studies  have shooperties ,41,42,43hns gene by IS sequences in 17978R. H-NS has been shown to be associated with bacterial resistance to a variety of antibiotics [hns deletion mutant, suggesting the regulation of TonB-dependent siderophores by H-NS. A study by Zhao et al. indicated that drug efflux may require a TonB or TonB-like protein for the operation of the outer membrane components and influence the multi-drug resistance of P. aeruginosa [hns or loss of plasmid containing hns, and thus increase the expression of TonB, then results in increased expression of the efflux pump.We also found that the expression of H-NS was significantly down-regulated in 17978R, and genomics analysis revealed that the cause was the loss of the plasmid and disruption of ibiotics ,45. It cibiotics . The abiibiotics . Free iribiotics . In our ruginosa . TherefoacrR. As an important regulator of the AcrAB efflux pump, AcrR can also be involved in regulating the motility, biofilm formation and virulence of Acinetobacter [anoR, which can specifically bind to it, thereby regulating the transcription of QS genes and indirectly regulating the expression of the pump AcrAB. As a repressor of the efflux pump AcrAB [In addition, the insertion of IS elements also caused the loss of tobacter . Researctobacter  found thmp AcrAB , AcrR camp AcrAB  have shohns in the plasmid and the disruption of the hns gene and acrR gene on the chromosome by the insertion of IS5. Loss of hns can result in increased expression level of TonB, which can increase the expression level of efflux pump. Previous studies have found some relationship between efflux pump and tigecycline resistance, however, no studies revealed the mechanism behind it. Taken together, the tigecycline resistance mechanism of A. baumannii is complex and we not only revealed the phenomenon that efflux pump is mainly responsible for tigecycline resistance, but also highlighted the mechanism at the genomic level. To our knowledge, this is the first study elaborating this issue from both proteomics and genomics analysis.In conclusion, in this study, we found a variety of mechanisms related to tigecycline resistance. The overwhelming data indicated that the mechanism of tigecycline resistance is complex. However, we still found a correlation between these results. Our study showed efflux pump is up-regulated in tigecycline-resistant strains and is the key mechanism for tigecycline resistance. By genomics analysis, we found several changes in the genome, which can explain this phenomenon , includiTigecycline-susceptible strain ATCC 17978 (named 17978S in this study) was incubated with sub-MIC tigecycline and selected for tigecycline-resistant strain 17978R according to a previous study . BrieflyThe stability test for strain’s tigecycline resistance was performed with broth dilution method. All the selected tigecycline-resistant and -susceptible strains were cultured in LB broth without antibiotics at 37 °C and sub-cultured in fresh broth every 24 h for 15 days. The MIC before and after passage was compared to see whether the strains have a stable tigecycline resistance phenotype. At the same time, the selected strain A54S was cultured on LB solid medium without antibiotics and LB solid medium containing 8 μg/mL tigecycline, the number of colonies were compared to test the recovery rate of tigecycline resistance.A. baumannii 17978S, 17978R, A54R, and A54S were grown to an optical density (OD600) of approximately 0.8 and then harvested using centrifugation at 8000× g for 10 min at 4 °C. The pellets were put into liquid nitrogen and grinded fully, and then suspended by phenol extraction solution with 1mM PMSF  and sonicated on ice. An equal volume of phenol-Tris-HCl (pH7.8) saturated solution were added and mixed up for 30 min at 4 °C, then the supernatants were collected by centrifugation at 4 °C, 7100× g for 10 min. Five times the volume of pre-cooled 0.1 M ammonium acetate-methanol solution were added and precipitated overnight at −20 °C. The precipitates were washed with pre-cooled methanol and acetone and collected by centrifugation at 4 °C, 12,000× g for 10 min and dried at room temperature, then dissolved in lysis solution at room temperature for 3 h and the concentration was determined by using a BCA Protein Assay Kit .Proteins from each sample (100 μg) were processed and subjected to isobaric tags for relative and absolute quantitation (iTRAQ) labeling using the AB SCIEX iTRAQ Regents Multiplex Kit  according to the manufacturer’s instructions. Briefly, proteins were first reduced and alkylated with DTT  and iodoacetamide , then digested with trypsin Trypsin-TPCK  at 37 °C overnight. The digested samples were lyophilized and reconstituted with 100 mM TEAB  and labeled with iTRAQ regent at room temperature for 2 h.2O  with a gradient of 0~8min, 98% A; 8~8.01 min, 98~95% A; 8.01~48 min, 95~75% A; 48~60 min, 75~60% A; 60~60.01 min, 60~10% A; 60.01~70 min, 10% A; 70~70.01 min, 10~98% A; 70.01~75 min, 98% A. The eluents of 8~60 min were collected every minute, freeze dried and saved for mass spectrometry. The Q Exactive HF mass spectrometer  was used in combination with the EASY-nLC 1200 LC system  for the separation and identification of proteomes. The sample was loaded into the precolumn Acclaim PepMap 100,100 μm × 2 cm RP-C18  at a flow rate of 300 nL/min, and then separated by the Acclaim Pepmap RSLC column, 75 μm × 15 cm RP-C18 . The quality resolution of the primary MS is 60,000, the automatic gain control value is 3e6, and the maximum injection time is 50 ms; the MS scanning is set as the full scanning charge mass ratio m/z range of 350–1500, and MS/MS scanning is performed for 20 of the highest peaks with resolution 15,000, automatic gain control 2e5, maximum ion injection time 40 ms, and the dynamic exclusion time 30 s. The data was deposited in ProteomeXchange with accession number PXD034784.The iTRAQ-labeled peptides were reconstituted and separated using reverse-phase 1100 HPLC  using an Agilent Zorbax Extend-C18 column  . In brief, an iTRAQ-labeled peptide mixture was eluted with ACN-Hp < 0.05 were considered to be differentially expressed. For the obtained differential proteins, NCBI BLAST+ and Uniprot databases were used for GO enrichment analysis . MS/MS spectra were searched using Proteome Discover2.4  software. Searches of protein identification were performed using the following criteria: static modification: iTRAQ , carbamidomethyl(C); dynamic modification: oxidation (M), Acetyl(N-term); digestion: trypsin; instrument: Q Exactive HF; MS1 tolerance: 10 ppm; MS2 tolerance: 0.02Da; missed cleavages:2. The credible proteins were screened according to the criteria of score sequence HT > 0 and unique peptide ≥1, and blank value was removed. Proteins with foldchange ≥2 or foldchange ≤1/2 and Efflux pump inhibitor PAβN  and CCCP  were used for the efflux pump inhibition experiment. The MICs of the bacteria were tested by either the dilution method in cation-regulated MH liquid medium containing 50 mg/mL PAβN or 10 mg/mL CCCP. The efflux pump inhibitory phenotype was considered positive when the MIC of a certain strain decreased by more than or equal to two folds in the presence of efflux pump inhibitor.http://sourceforge.net/projects/brig, assessed on 2 March 2022) and Mauve  were used for genome linear analysis. IS elements were predicted using ISEScan [Total genomic DNA was extracted from overnight cultures using the Bacterial DNA Isolation Kit  following the manufacturer’s instructions, followed by genomic DNA sequencing using the Illumina Hiseq™ sequencer  and PacBio RSII system . FastQC (Version 0.11.2) was used for quality control. Adaptor sequences were trimmed and removed using Trimmomatic. The short-read sequence was assembled using SPAdes v3.10.0 . GapFill ISEScan  with defGene knockouts were performed as described previously . Briefly